Latent multi-state models for non-equidistant longitudinal observations with finite and infinite mixture model-based clustering
Bibliographic record
Abstract
Large amounts of data that exist in the form of longitudinal health records, such as electronic health records (EHRs), healthcare administrative databases and mobile health applications, are now available for dynamic monitoring of the underlying processes governing the observations.However, such latent progression generating the observations is not observed directly and so requires inferential methods to ascertain progression.Moreover, records are only observed when a subject interacts with the healthcare system, resulting in irregular visits where the observations are not collected at equidistant time intervals with possible sparsity.For example, in healthcare databases, chronic disease patients do not seek intensive care at early stage of the disease, and therefore the records may be sparse, and patients might seek care outside the healthcare system, which means that only a segment of the entire health trajectory might be observed.These considerations suggest that trajectories should be modeled as a latent continuous-time process.The progression usually depends on the evolution of different types of time-varying covariates.Incorporating these covariates into the model can advance our understanding of the development of the con-time here enjoyable and made it through my PhD.Last but not least, I would like to give the most credit to my parents who are always supporting and encouraging me the best they can.From my undergraduate in Waterloo to doctoral study in Montreal, they provided me with the emotional and financial support that I needed to keep going.Without them, I would not be where I am today.This thesis is dedicated to them for their love, endless support, understanding and encouragement that I will cherish for my lifetime.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".